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language: en
license: apache-2.0
tags:
- text2text-generation
- crop-recommendation
- agriculture
- lora
- mt5
---
# 🌱 mT5 Crop Recommendation (LoRA Fine-tuned)
This is a fine-tuned [mT5](https://huggingface.co/google/mt5-base) model using **LoRA adapters** for crop recommendation tasks.
It takes weather and environmental inputs and suggests the most suitable crop(s) along with profitability insights.
## 🧑🏫 Model Details
- **Base Model**: `google/mt5-base`
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Framework**: 🤗 Transformers
- **Dataset**: Custom crop recommendation dataset (weather, soil, profitability annotations)
- **Languages**: English
## 📊 Training
- **Epochs**: ~0.1–1 (early stop around loss 0.1)
- **Trainable Parameters**: ~344K (LoRA only)
- **Total Parameters**: ~300M
- **Hardware**: A100 GPU (Colab)
## 🔎 Example Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "your-username/mt5-crop-lora"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
query = "Suggest best crop given: rainfall=200mm, temperature=25C, soil=loamy"
inputs = tokenizer(query, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
**Sample Output:**
```
Best crop: Gram.
Top 3: Gram, Mustard, Wheat.
Weather outlook: Cooler, dry weather with lower rainfall.
Profitability: Moderate.
```
## ✅ Use Cases
* Agricultural planning
* Crop advisory chatbots
* Climate-aware farming assistance
## ⚠️ Limitations
* Limited dataset → may not generalize globally
* Should not replace expert advice
* Cross-check with local agronomic data
## 📜 License
Apache 2.0
## 🙌 Acknowledgements
* [Google Research](https://huggingface.co/google) for mT5
* Hugging Face Transformers & PEFT
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